Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithm crossover operators for ordering applications
Computers and Operations Research - Special issue on genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
ASPARAGOS An Asynchronous Parallel Genetic Optimization Strategy
Proceedings of the 3rd International Conference on Genetic Algorithms
Distributed Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Distributed Approach for Implementing Genetic Algorithms
ICPP '94 Proceedings of the 1994 International Conference on Parallel Processing - Volume 03
| Hi-index | 0.00 |
The paper presents a distributed genetic algorithm implementation for obtaining good quality consistent results for different ordering problems. Most importantly, the solution found by the proposed Distributed GA is not only of high quality but also robust and does not require fine tuning of the probabilities of crossover and mutation. In addition, implementation of the Distributed GA is simple and does not require the use of any specialized, expensive hardware. Fault tolerance has also been provided by dynamic reconfiguration of the distributed system in the event of a process or machine failure. The effectiveness of using a simple crossover scheme with Distributed GA is demonstrated by solving three variations of the Traveling Salesman Problem (TSP).